The great success of deep learning techniques significantly promotes the
development of end-to-end learned video coding. For the deep learning based coding methods, they do not rely
on the partition scheme and support full-resolution coding,
which naturally avoids the blocking artifacts. Generally, the representative and powerful feature is extracted via a hierarchical network and jointly optimized with the reconstruction task for high
efficient coding. For instance, the early work [1]} focuses on motion predictive coding and proposes the concept of PixelMotionCNN
(PMCNN) to model spatiotemporal coherence to effectively perform predictive coding inside the learning network. Similarly, recurrent neural network [2]}, [3]}, VAE generative model [4]}, [5]} and non-local attention [6]}, [7]} are employed to remove the unnecessary spatial redundancy from the latent representations to make features compact, and thus leading to improved coding performance.
